A New Qualitative GIS Method for Investigating Neighbourhood Characteristics Using a Tablet
Bibliographic record
Abstract
This article presents a methodological and technical reflection on an innovative and interactive qualitative geographic information systems (GIS) tool and method created to gauge people's images and perceptions of their neighbourhood. Knowledge gained from the critical GIS debates has led to the development of qualitative GIS and public participation GIS (PPGIS) methods, which aim to counteract the adverse effects of GIS as predominantly top-down. Drawing from critical and qualitative GIS arguments, the authors tried to create an accessible, bottom-up GIS data-collection method that involved conducting qualitative interviews while presenting digital maps on a tablet. This digital tool allowed users to change scales by zooming in and out on the map and also offered a selection of base maps affording numerous views of the city. This method not only allowed residents to generate GIS data about their neighbourhood but was also used as a visual support tool to stimulate dialogue during the interviews. With the aid of examples from a study in Geneva, Switzerland, this article discusses the relevance, strengths, and limitations of this method in the field of neighbourhood research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".